Within our A-STAR\* (Aerospace Standardisation Technology for Assembly and Repair) project we aim to deliver productivity, material and energy efficiency improvements in aerospace manufacturing by focusing on the development and demonstration of VIOLET: an innovative Artificial Intelligence (AI) assembly assistant based on advanced computer vision, capable of automatically checking digital manufacture route-cards: * automatic defect detection thereby reducing the in-process assembly errors * traceability & compliance via automatically generated historical records * automatic skill capture on the factory floor for operator training and knowledge dissemination The project will start with a focus on material and energy-intensive aerospace assembly processes with Dunlop Aircraft Tyres as the lead demonstrator - the UK's largest Aerospace Tyre Manufacturer - and then demonstrate aerospace scalability through deployment and testing at Brookhouse Aerospace, a leading manufacturer of composite structures for civil and defence. A-STAR will leverage state-of-the-art AI computer vision to recognise operator actions on assembly processes to ensure that stringent aerospace quality standards are met. The use of convolutional neural networks offers more generalisability for pattern recognition and performs better for detecting anomalies compared to traditional automated optical inspection. A digital record of the actions that have been performed on a product can then be stored as a proof of quality management as well as be used to train new special-process operators. This record is useful for digitally connecting factories so that defects can be traced through supply-chains and used to prove quality standards. We are innovating beyond state-of-the-art computer vision and human action recognition, bringing AI to human action data in aerospace manual processes and in the long-term advancing the way in which people work in manufacturing. When delivered, A-STAR will become the template for AI-assisted assembly, delivering energy and material reduction and enable a sustainable, energy-resilient approach to assembly and repair operations.

Environment Centred Optimisation of SME Productivity using Realtime INTelligence (ECO-SPRINT) - focuses on the aligned sustainability & productivity needs of manufacturing SMEs. It brings together a consortium of IDT providers that focus on manufacturing data & intelligence, eager to build sustainability into their offerings. The project will start with a focus on energy-intensive aerospace special finishing processes (with Poetons as the lead demonstrator - the UK's largest independent surface treatment business), and then demonstrate scalability through deployment in another sector courtesy of Metal Assemblies Ltd. Recognising that many processes are hard to automate, it will develop an innovative vision intelligence system (VIOLET) combined with a scalable and rapid-to-deploy IIoT solution. It will integrate this with an innovative combination of IDTs onto which sustainability intelligence capability will be integrated including a Smart Manufacturing Execution System (MES); a "customer/supplier portal" which shares intelligence on demand volatility, an advanced scheduling system, an asset protection system, and a configurable Business Intelligence (BI) solution. The outputs from ECO-SPRINT will enable SMEs to: -capture unprecedented business intelligence about human operations which will feed into the consortium's scheduling software and enable optimisation of manual processes such as casting plants, electroplating vats and heat treatment oven utilisation -capture plant and energy utilisation data enabling real-time optimisation -digitise human action from skilled special-process operators to provide automated time, motion and risk capture data -extract and segment videos of human actions on the special-process house optimisation of plant and utilities to retain best-practice through digitalised knowledge transfer -gather data on carbon and material footprint that can be communicated and aggregated across supply chains (eg. through Valuechain's supplier and customer portals)

This project addresses 2 Government priorities...improving productivity of manufacturing SMEs and reducing carbon footprint to achieve net zero by 2050\. A major capex cost for manufacturing SMEs is new plant & equipment such as CNC or AM machines. However due to a lack of real-time machine data, business intelligence applications and planning systems, many SMEs operate under utilised plant which not only impacts productivity but also wastes energy. Machine monitoring solutions have been on the market for more than 20 years however, despite the potential impact on productivity, their adoption has been limited especially within the SME sector. We surveyed a representative sample of our 400+ UK manufacturing clients to understand why they had not adopted machine monitoring technologies previously and their reservations were as follows: * cost of hardware and software * complexity and cost of plant integration and implementation services * lack of systems flexibility Fitfactory have started to address these challenges by developing a super low cost vibration detection sensor that can be attached to the outside of any machine using magnets. Our Fitfactory Business Intelligence application -- Insights - connects seamlessly with disparate ERP systems and our Rapid IOT vibration sensor to not only present utilisation data and automate alerts but also generates actionable insights and risks for production teams to monitor and add their lessons learned. However, to create a game-changing platform, we must deploy machine learning AI that can be easily applied for multiple use cases, by taking the sensor vibration data, and analysing the lessons learned, to create an intelligent prediction tool that empowers operators so that they can make more informed shop floor planning decisions to minimise downtime and energy usage. By collaborating with a leading UK RTO, we are confident that we can overcome the complex analytical challenges to create an intuitive platform that can be rapidly configured and deployed at scale to accelerate the optimisation of SME manufacturing plant utilisation by at least 10% and reduce energy usage by 5%. STFC will provide specialist support to create a flexible machine learning solution that be applied to all manufacturing plant and equipment that can be quickly configured, at minimum cost, using a simple user interface for production operators to capture various qualitative root cause analysis reasons for downtime, combined with quantitative plant utilisation analysis to predict machine tool wear and to mitigate downtime through improved planning decision support.

There is an immediate need for granular supply-chain intelligence and proactive risk mitigation through collaboration and focused intervention, to prevent good UK aerospace, automotive and other HVM SMEs from getting into unrecoverable financial difficulties. However, in order to mitigate impact of Covid-19, UK aerospace and automotive manufacturing businesses can no longer work in silos and compete as individual organisations. Instead, they must connect and collaborate with customers and suppliers so that they can compete as integrated, cross-sector manufacturing ecosystems, which enables organisations to focus on core competences, benefit from economies-of-scale and share knowledge. This project will address significant barriers that inhibit growth of cross-sector manufacturing ecosystems; such as supply chain transparency, data security, trust, engagement and lack of digitalised infrastructure to streamline the capture, curation and analysis of reliable supply-chain big data. **ACCESS Innovations:** * launch a cross-sector SME-focused supply-chain collaboration digital marketplace that will enable B2B tendering and collaboration * establish a freemium smart manufacturing app-store that will drive SME engagement and incentivise users to maintain profile data, connect with other network users and drive collaboration * generate cross-sector classification taxonomy that underpins B2B matchmaking AI to automatically recommend connections for work package tenders and innovation consortium **ACCESS Outcomes:** * unprecedented multi-tier visibility of UK aerospace and automotive supply-chain which will reduce sub-tier risks and increase on-shoring opportunities * connect aerospace and automotive suppliers with cross-sector high growth opportunities such as rail, sustainable transport and renewable energy supply-chains * enable aerospace and automotive manufacturing SMEs to identify new sustainable supply-chain opportunities, to catalyse business re-purposing, mitigate business downturn and loss of critical skills * provide UK OEMs and Government bodies with unprecedented granular industry intelligence to focus policy and intervention support, ensuring UK taxpayer's money deployed most effectively to re-grow UK aerospace and automotive manufacturing whilst focusing on sustainability and green agenda.

To reduce the spread of Covid-19 it widely acknowledged that social distancing plays a critical role. However, due to the criticality on the UK economy it is envisaged that most manufacturing companies will be encouraged to restart production well in advance of the UK beating Covid-19\. It is therefore essential that UK manufacturers are able to protect their employees and proactively encourage social distancing. This project combines low cost off-the-shelf sensors worn in tabards around the necks of employees, with a scalable monitoring app, factory visualisation dashboard and an innovative gamification approach underpinned by AI, to provide real-time monitoring and Covid-19 HSE compliance scores by employee, department or company. Tracking systems can be set up which rely only upon employees 'opting-in' to allowing their employers to track them using their personal or work-issued mobile phones through either a dedicated app, or simply by registering a device on the WiFi network. However, this technique offers limited positional accuracy and is best for sparsely populated workspaces. For privacy sake, this ability is limited to proximity to the work WiFi network. Better accuracy can be achieved with Bluetooth Low Energy (BLE) tracking. While this option does require capital expenditure, the costs are modest and would account for an improvement of a facility infrastructure. Fixed BLE 'stations' are deployed throughout a facility and interact with personal BLE devices on a continuous basis to determine location. Personal mobile devices can be used, as in the WiFi tracking method, but small, unobtrusive dedicated body-worn sensors can also be purchased for this purpose. The dedicated units have a number of advantages over the opt-in use of personal devices, including 100% compliance and the ability to sanitise devices effectively in sanitation stations overnight or between shifts to allow a 'pool' of communal sensors to be used. The SmartApart app will continually monitor Covid-19 social distancing which will be communicated to workforce employees via digital shopfloor dashboards as well as via an app on their smart phones. The critical success of this project is ensuring that the sensor technology is very low-cost, simple to implement and use, and has a gamification element to encourage individuals and organisations to focus on social distancing.

Capturing 3Dprinting big data from plant, materials, sensors and ERP/PLM systems. Deploying artificial intelligence to optimise product quality, productivity and energy consumption. Standardising Al powered manufacturing execution system to enable global aerospace SMEs to scale up 3D printing.